Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When fabricating, it is common to follow a prescribed set of steps in a tutorial or how-to. While popular, such explicit knowledge resources have many inconsistencies and omissions, use static illustrations, and cannot adapt to drop-in makers or a maker's mistakes. To overcome many of these issues, this work presents Automatics, a novel explicit knowledge resource system that dynamically generates fabrication activities for one or more makers based on their current environmental and fabrication context. Automatics assigns tasks to makers based on the past tools and components the maker was working with, enables makers to recover from mistakes through model regeneration, suggests alternative tools if a needed tool is unavailable or in use, and allows multiple makers to drop-in throughout a fabrication activity. Initial usage and feedback from novice makers showed that Automatics increases the number of tasks that can be completed compared to paper instructions, decreases frustration, and improves one's understanding of the global context of assigned tasks during fabrication activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it